| |
| import gc |
| import logging |
| import math |
| import os |
| import random |
| import sys |
| import types |
| from contextlib import contextmanager |
| from functools import partial |
|
|
| import torch |
| import torch.cuda.amp as amp |
| import torch.distributed as dist |
| from tqdm import tqdm |
|
|
| from .distributed.fsdp import shard_model |
| from .distributed.sequence_parallel import sp_attn_forward, sp_dit_forward |
| from .distributed.util import get_world_size |
| from .modules.model import WanModel |
| from .modules.t5 import T5EncoderModel |
| from .modules.vae2_1 import Wan2_1_VAE |
| from .utils.fm_solvers import ( |
| FlowDPMSolverMultistepScheduler, |
| get_sampling_sigmas, |
| retrieve_timesteps, |
| ) |
| from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler |
|
|
|
|
| class WanT2V: |
|
|
| def __init__( |
| self, |
| config, |
| checkpoint_dir, |
| device_id=0, |
| rank=0, |
| t5_fsdp=False, |
| dit_fsdp=False, |
| use_sp=False, |
| t5_cpu=False, |
| init_on_cpu=True, |
| convert_model_dtype=False, |
| ): |
| r""" |
| Initializes the Wan text-to-video generation model components. |
| |
| Args: |
| config (EasyDict): |
| Object containing model parameters initialized from config.py |
| checkpoint_dir (`str`): |
| Path to directory containing model checkpoints |
| device_id (`int`, *optional*, defaults to 0): |
| Id of target GPU device |
| rank (`int`, *optional*, defaults to 0): |
| Process rank for distributed training |
| t5_fsdp (`bool`, *optional*, defaults to False): |
| Enable FSDP sharding for T5 model |
| dit_fsdp (`bool`, *optional*, defaults to False): |
| Enable FSDP sharding for DiT model |
| use_sp (`bool`, *optional*, defaults to False): |
| Enable distribution strategy of sequence parallel. |
| t5_cpu (`bool`, *optional*, defaults to False): |
| Whether to place T5 model on CPU. Only works without t5_fsdp. |
| init_on_cpu (`bool`, *optional*, defaults to True): |
| Enable initializing Transformer Model on CPU. Only works without FSDP or USP. |
| convert_model_dtype (`bool`, *optional*, defaults to False): |
| Convert DiT model parameters dtype to 'config.param_dtype'. |
| Only works without FSDP. |
| """ |
| self.device = torch.device(f"cuda:{device_id}") |
| self.config = config |
| self.rank = rank |
| self.t5_cpu = t5_cpu |
| self.init_on_cpu = init_on_cpu |
|
|
| self.num_train_timesteps = config.num_train_timesteps |
| self.boundary = config.boundary |
| self.param_dtype = config.param_dtype |
|
|
| if t5_fsdp or dit_fsdp or use_sp: |
| self.init_on_cpu = False |
|
|
| shard_fn = partial(shard_model, device_id=device_id) |
| self.text_encoder = T5EncoderModel( |
| text_len=config.text_len, |
| dtype=config.t5_dtype, |
| device=torch.device('cpu'), |
| checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint), |
| tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer), |
| shard_fn=shard_fn if t5_fsdp else None) |
|
|
| self.vae_stride = config.vae_stride |
| self.patch_size = config.patch_size |
| self.vae = Wan2_1_VAE( |
| vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint), |
| device=self.device) |
|
|
| logging.info(f"Creating WanModel from {checkpoint_dir}") |
| self.low_noise_model = WanModel.from_pretrained( |
| checkpoint_dir, subfolder=config.low_noise_checkpoint) |
| self.low_noise_model = self._configure_model( |
| model=self.low_noise_model, |
| use_sp=use_sp, |
| dit_fsdp=dit_fsdp, |
| shard_fn=shard_fn, |
| convert_model_dtype=convert_model_dtype) |
|
|
| self.high_noise_model = WanModel.from_pretrained( |
| checkpoint_dir, subfolder=config.high_noise_checkpoint) |
| self.high_noise_model = self._configure_model( |
| model=self.high_noise_model, |
| use_sp=use_sp, |
| dit_fsdp=dit_fsdp, |
| shard_fn=shard_fn, |
| convert_model_dtype=convert_model_dtype) |
| if use_sp: |
| self.sp_size = get_world_size() |
| else: |
| self.sp_size = 1 |
|
|
| self.sample_neg_prompt = config.sample_neg_prompt |
|
|
| def _configure_model(self, model, use_sp, dit_fsdp, shard_fn, |
| convert_model_dtype): |
| """ |
| Configures a model object. This includes setting evaluation modes, |
| applying distributed parallel strategy, and handling device placement. |
| |
| Args: |
| model (torch.nn.Module): |
| The model instance to configure. |
| use_sp (`bool`): |
| Enable distribution strategy of sequence parallel. |
| dit_fsdp (`bool`): |
| Enable FSDP sharding for DiT model. |
| shard_fn (callable): |
| The function to apply FSDP sharding. |
| convert_model_dtype (`bool`): |
| Convert DiT model parameters dtype to 'config.param_dtype'. |
| Only works without FSDP. |
| |
| Returns: |
| torch.nn.Module: |
| The configured model. |
| """ |
| model.eval().requires_grad_(False) |
|
|
| if use_sp: |
| for block in model.blocks: |
| block.self_attn.forward = types.MethodType( |
| sp_attn_forward, block.self_attn) |
| model.forward = types.MethodType(sp_dit_forward, model) |
|
|
| if dist.is_initialized(): |
| dist.barrier() |
|
|
| if dit_fsdp: |
| model = shard_fn(model) |
| else: |
| if convert_model_dtype: |
| model.to(self.param_dtype) |
| if not self.init_on_cpu: |
| model.to(self.device) |
|
|
| return model |
|
|
| def _prepare_model_for_timestep(self, t, boundary, offload_model): |
| r""" |
| Prepares and returns the required model for the current timestep. |
| |
| Args: |
| t (torch.Tensor): |
| current timestep. |
| boundary (`int`): |
| The timestep threshold. If `t` is at or above this value, |
| the `high_noise_model` is considered as the required model. |
| offload_model (`bool`): |
| A flag intended to control the offloading behavior. |
| |
| Returns: |
| torch.nn.Module: |
| The active model on the target device for the current timestep. |
| """ |
| if t.item() >= boundary: |
| required_model_name = 'high_noise_model' |
| offload_model_name = 'low_noise_model' |
| else: |
| required_model_name = 'low_noise_model' |
| offload_model_name = 'high_noise_model' |
| if offload_model or self.init_on_cpu: |
| if next(getattr( |
| self, |
| offload_model_name).parameters()).device.type == 'cuda': |
| getattr(self, offload_model_name).to('cpu') |
| if next(getattr( |
| self, |
| required_model_name).parameters()).device.type == 'cpu': |
| getattr(self, required_model_name).to(self.device) |
| return getattr(self, required_model_name) |
|
|
| def generate(self, |
| input_prompt, |
| size=(1280, 720), |
| frame_num=81, |
| shift=5.0, |
| sample_solver='unipc', |
| sampling_steps=50, |
| guide_scale=5.0, |
| n_prompt="", |
| seed=-1, |
| offload_model=True): |
| r""" |
| Generates video frames from text prompt using diffusion process. |
| |
| Args: |
| input_prompt (`str`): |
| Text prompt for content generation |
| size (`tuple[int]`, *optional*, defaults to (1280,720)): |
| Controls video resolution, (width,height). |
| frame_num (`int`, *optional*, defaults to 81): |
| How many frames to sample from a video. The number should be 4n+1 |
| shift (`float`, *optional*, defaults to 5.0): |
| Noise schedule shift parameter. Affects temporal dynamics |
| sample_solver (`str`, *optional*, defaults to 'unipc'): |
| Solver used to sample the video. |
| sampling_steps (`int`, *optional*, defaults to 50): |
| Number of diffusion sampling steps. Higher values improve quality but slow generation |
| guide_scale (`float` or tuple[`float`], *optional*, defaults 5.0): |
| Classifier-free guidance scale. Controls prompt adherence vs. creativity. |
| If tuple, the first guide_scale will be used for low noise model and |
| the second guide_scale will be used for high noise model. |
| n_prompt (`str`, *optional*, defaults to ""): |
| Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` |
| seed (`int`, *optional*, defaults to -1): |
| Random seed for noise generation. If -1, use random seed. |
| offload_model (`bool`, *optional*, defaults to True): |
| If True, offloads models to CPU during generation to save VRAM |
| |
| Returns: |
| torch.Tensor: |
| Generated video frames tensor. Dimensions: (C, N H, W) where: |
| - C: Color channels (3 for RGB) |
| - N: Number of frames (81) |
| - H: Frame height (from size) |
| - W: Frame width from size) |
| """ |
| |
| guide_scale = (guide_scale, guide_scale) if isinstance( |
| guide_scale, float) else guide_scale |
| F = frame_num |
| target_shape = (self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1, |
| size[1] // self.vae_stride[1], |
| size[0] // self.vae_stride[2]) |
|
|
| seq_len = math.ceil((target_shape[2] * target_shape[3]) / |
| (self.patch_size[1] * self.patch_size[2]) * |
| target_shape[1] / self.sp_size) * self.sp_size |
|
|
| if n_prompt == "": |
| n_prompt = self.sample_neg_prompt |
| seed = seed if seed >= 0 else random.randint(0, sys.maxsize) |
| seed_g = torch.Generator(device=self.device) |
| seed_g.manual_seed(seed) |
|
|
| if not self.t5_cpu: |
| self.text_encoder.model.to(self.device) |
| context = self.text_encoder([input_prompt], self.device) |
| context_null = self.text_encoder([n_prompt], self.device) |
| if offload_model: |
| self.text_encoder.model.cpu() |
| else: |
| context = self.text_encoder([input_prompt], torch.device('cpu')) |
| context_null = self.text_encoder([n_prompt], torch.device('cpu')) |
| context = [t.to(self.device) for t in context] |
| context_null = [t.to(self.device) for t in context_null] |
|
|
| noise = [ |
| torch.randn( |
| target_shape[0], |
| target_shape[1], |
| target_shape[2], |
| target_shape[3], |
| dtype=torch.float32, |
| device=self.device, |
| generator=seed_g) |
| ] |
|
|
| @contextmanager |
| def noop_no_sync(): |
| yield |
|
|
| no_sync_low_noise = getattr(self.low_noise_model, 'no_sync', |
| noop_no_sync) |
| no_sync_high_noise = getattr(self.high_noise_model, 'no_sync', |
| noop_no_sync) |
|
|
| |
| with ( |
| torch.amp.autocast('cuda', dtype=self.param_dtype), |
| torch.no_grad(), |
| no_sync_low_noise(), |
| no_sync_high_noise(), |
| ): |
| boundary = self.boundary * self.num_train_timesteps |
|
|
| if sample_solver == 'unipc': |
| sample_scheduler = FlowUniPCMultistepScheduler( |
| num_train_timesteps=self.num_train_timesteps, |
| shift=1, |
| use_dynamic_shifting=False) |
| sample_scheduler.set_timesteps( |
| sampling_steps, device=self.device, shift=shift) |
| timesteps = sample_scheduler.timesteps |
| elif sample_solver == 'dpm++': |
| sample_scheduler = FlowDPMSolverMultistepScheduler( |
| num_train_timesteps=self.num_train_timesteps, |
| shift=1, |
| use_dynamic_shifting=False) |
| sampling_sigmas = get_sampling_sigmas(sampling_steps, shift) |
| timesteps, _ = retrieve_timesteps( |
| sample_scheduler, |
| device=self.device, |
| sigmas=sampling_sigmas) |
| else: |
| raise NotImplementedError("Unsupported solver.") |
|
|
| |
| latents = noise |
|
|
| arg_c = {'context': context, 'seq_len': seq_len} |
| arg_null = {'context': context_null, 'seq_len': seq_len} |
|
|
| for _, t in enumerate(tqdm(timesteps)): |
| latent_model_input = latents |
| timestep = [t] |
|
|
| timestep = torch.stack(timestep) |
|
|
| model = self._prepare_model_for_timestep( |
| t, boundary, offload_model) |
| sample_guide_scale = guide_scale[1] if t.item( |
| ) >= boundary else guide_scale[0] |
|
|
| noise_pred_cond = model( |
| latent_model_input, t=timestep, **arg_c)[0] |
| noise_pred_uncond = model( |
| latent_model_input, t=timestep, **arg_null)[0] |
|
|
| noise_pred = noise_pred_uncond + sample_guide_scale * ( |
| noise_pred_cond - noise_pred_uncond) |
|
|
| temp_x0 = sample_scheduler.step( |
| noise_pred.unsqueeze(0), |
| t, |
| latents[0].unsqueeze(0), |
| return_dict=False, |
| generator=seed_g)[0] |
| latents = [temp_x0.squeeze(0)] |
|
|
| x0 = latents |
| if offload_model: |
| self.low_noise_model.cpu() |
| self.high_noise_model.cpu() |
| torch.cuda.empty_cache() |
| if self.rank == 0: |
| videos = self.vae.decode(x0) |
|
|
| del noise, latents |
| del sample_scheduler |
| if offload_model: |
| gc.collect() |
| torch.cuda.synchronize() |
| if dist.is_initialized(): |
| dist.barrier() |
|
|
| return videos[0] if self.rank == 0 else None |
|
|
